Scikit Image

scikit-image is a Python image processing library and scientific image analysis toolkit. It provides a framework for digital image processing and computer vision, utilizing numerical arrays for pixel-level manipulations.

The library enables the quantification of image properties and the detection of visual features, such as edges and blobs. It includes tools for image segmentation and the extraction of textures and patterns to characterize objects within visual data.

Capabilities cover image manipulation through color space conversion, geometric transformations, and digital restoration. It also provides utilities for morphological operations, image registration, and the processing of video files.

The project uses a plugin system for importing and exporting image files across various formats.

Features

NumPy Image Processors - Leverages NumPy multidimensional arrays to perform high-performance vectorized pixel-level image manipulations.
Scientific Image Analysis Toolkits - Provides a comprehensive suite of tools for quantifying image properties and analyzing visual data for scientific research.
Computer Vision Libraries - Provides a comprehensive library of computer vision tools, including Hough transforms and SIFT descriptors.
Image Segmentation - Partitions images into distinct clusters based on color, intensity, or texture to facilitate visual analysis.
Classical Edge Detection - Implements classical operators like Canny and Hough transforms to identify boundaries and linear structures.
Feature Detection - Provides algorithms for identifying and extracting key points and distinct structures like blobs from images.
Image Geometric Transformations - Provides comprehensive tools for rotating, scaling, and warping visual data through affine transformations and interpolation.
2D Image Transformations - Provides matrix-based 2D transformations for scaling, rotating, and aligning images.
Convolutional Filtering - Ships a wide array of kernel-based convolution filters for noise reduction, sharpening, and edge detection.
Morphological Operations - Implements morphological operations like erosion and dilation using structuring elements to modify binary and grayscale shapes.
Image Feature Extraction - Provides tools to extract visual features, edges, and textures to characterize image objects.
Image Property Measurements - Calculates quantitative attributes such as area and intensity for specific image regions.
Scientific Quantitative Analysis - Extracts quantitative measurements and geometric properties to analyze scientific and biological samples.
Digital Image Processing - Implements a full framework for digital image processing, including color space manipulation and morphological operations.
Image Transformation Utilities - Resizes, rotates, and warps images using affine transformations and interpolation.
Geometric Transformation Routines - Applies linear transformations including scaling, rotation, and skewing to visual data.
Computer Vision Preprocessing - Includes preprocessing utilities to normalize color and remove noise for computer vision pipelines.
Image Registration - Aligns and maps multiple images into a single coordinate system using optical flow or stitching.
Connected Component Analysis - Implements algorithms to identify and group connected pixel components for structural and connectivity analysis.
Feature-Based Image Alignment - Aligns images by comparing visual descriptors from linear segments.
Image and Video Restoration Suites - Improves visual fidelity through denoising, inpainting, and deblurring frameworks.
Contrast Enhancements - Modifies contrast and brightness using intensity redistribution to normalize lighting.
Image Import and Export - Supports loading and saving image data across various file formats via a flexible plugin system.
Image Noise Reduction - Removes chrominance and luminance artifacts using smoothing and sharpening kernels.
Image Restoration - Implements digital restoration filters to remove blur and noise from degraded images.
Image Similarity Estimation - Computes metrics to quantify the resemblance and correlation between separate images.
Color Space Conversions - Transforms images between different color models such as RGB, HSV, and grayscale.
Image Topology Analysis - Analyzes shapes and structures using connected component labeling and distance transforms.
Region Geometric Analysis - Measures perimeters, areas, and geometric attributes of segmented image objects to quantify physical characteristics.
Color Adjustment Utilities - Adjusts contrast and exposure using gamma curves and histogram equalization to improve visibility.
Image Histogram Equalization - Provides histogram equalization and redistributive intensity transforms to optimize image contrast and exposure.
Image Format Plugins - Uses a plugin-based architecture to support loading and saving image files across diverse formats.
Computer Vision - Image processing toolbox for scientific computing.
General Machine Learning - Image processing algorithms for Python.
Machine-Learning-Frameworks - Image processing algorithms for scientific computing.
Machine Learning Packages - Image processing algorithms for Python.
Feature Engineering - Comprehensive library for image processing tasks.
Image Processing - Offers advanced algorithms for scientific image processing.

Star-Verlauf

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Name: scikit-image/scikit-image
Author: scikit-image

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6,529 Stars2,385 ForksPython3 Aufrufescikit-image.org

Scikit Image

The project uses a plugin system for importing and exporting image files across various formats.

Features

NumPy Image Processors - Leverages NumPy multidimensional arrays to perform high-performance vectorized pixel-level image manipulations.
Scientific Image Analysis Toolkits - Provides a comprehensive suite of tools for quantifying image properties and analyzing visual data for scientific research.
Computer Vision Libraries - Provides a comprehensive library of computer vision tools, including Hough transforms and SIFT descriptors.
Image Segmentation - Partitions images into distinct clusters based on color, intensity, or texture to facilitate visual analysis.
Classical Edge Detection - Implements classical operators like Canny and Hough transforms to identify boundaries and linear structures.
Feature Detection - Provides algorithms for identifying and extracting key points and distinct structures like blobs from images.
Image Geometric Transformations - Provides comprehensive tools for rotating, scaling, and warping visual data through affine transformations and interpolation.
2D Image Transformations - Provides matrix-based 2D transformations for scaling, rotating, and aligning images.
Convolutional Filtering - Ships a wide array of kernel-based convolution filters for noise reduction, sharpening, and edge detection.
Morphological Operations - Implements morphological operations like erosion and dilation using structuring elements to modify binary and grayscale shapes.
Image Feature Extraction - Provides tools to extract visual features, edges, and textures to characterize image objects.
Image Property Measurements - Calculates quantitative attributes such as area and intensity for specific image regions.
Scientific Quantitative Analysis - Extracts quantitative measurements and geometric properties to analyze scientific and biological samples.
Digital Image Processing - Implements a full framework for digital image processing, including color space manipulation and morphological operations.
Image Transformation Utilities - Resizes, rotates, and warps images using affine transformations and interpolation.
Geometric Transformation Routines - Applies linear transformations including scaling, rotation, and skewing to visual data.
Computer Vision Preprocessing - Includes preprocessing utilities to normalize color and remove noise for computer vision pipelines.
Image Registration - Aligns and maps multiple images into a single coordinate system using optical flow or stitching.
Connected Component Analysis - Implements algorithms to identify and group connected pixel components for structural and connectivity analysis.
Feature-Based Image Alignment - Aligns images by comparing visual descriptors from linear segments.
Image and Video Restoration Suites - Improves visual fidelity through denoising, inpainting, and deblurring frameworks.
Contrast Enhancements - Modifies contrast and brightness using intensity redistribution to normalize lighting.
Image Import and Export - Supports loading and saving image data across various file formats via a flexible plugin system.
Image Noise Reduction - Removes chrominance and luminance artifacts using smoothing and sharpening kernels.
Image Restoration - Implements digital restoration filters to remove blur and noise from degraded images.
Image Similarity Estimation - Computes metrics to quantify the resemblance and correlation between separate images.
Color Space Conversions - Transforms images between different color models such as RGB, HSV, and grayscale.
Image Topology Analysis - Analyzes shapes and structures using connected component labeling and distance transforms.
Region Geometric Analysis - Measures perimeters, areas, and geometric attributes of segmented image objects to quantify physical characteristics.
Color Adjustment Utilities - Adjusts contrast and exposure using gamma curves and histogram equalization to improve visibility.
Image Histogram Equalization - Provides histogram equalization and redistributive intensity transforms to optimize image contrast and exposure.
Image Format Plugins - Uses a plugin-based architecture to support loading and saving image files across diverse formats.
Computer Vision - Image processing toolbox for scientific computing.
General Machine Learning - Image processing algorithms for Python.
Machine-Learning-Frameworks - Image processing algorithms for scientific computing.
Machine Learning Packages - Image processing algorithms for Python.
Feature Engineering - Comprehensive library for image processing tasks.
Image Processing - Offers advanced algorithms for scientific image processing.

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Häufig gestellte Fragen

Was macht scikit-image/scikit-image?

Was sind die Hauptfunktionen von scikit-image/scikit-image?

Die Hauptfunktionen von scikit-image/scikit-image sind: NumPy Image Processors, Scientific Image Analysis Toolkits, Computer Vision Libraries, Image Segmentation, Classical Edge Detection, Feature Detection, Image Geometric Transformations, 2D Image Transformations.

Welche Open-Source-Alternativen gibt es zu scikit-image/scikit-image?

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